The purpose of this review is to evaluate existing learning agility measures and offer recommendations for their use in organizational and scholarly contexts.
This is a general review paper assessing the psychometric qualities of prevalent learning agility measures. Measures were selected based on their predominance and use in the learning agility literature and organizational settings.
Learning agility measurement is an area requiring further research. Multiple conceptualizations of learning agility exist, making the true structure of learning agility unclear. The learning agility measures in the academic literature deviate from learning agility’s traditional conceptualization and require further validation and convergent validity studies. Commercial measures of learning agility exist, but their development procedures are not subjected to peer review and are not widely used in academic research, given the cost associated with their use.
Learning agility is prevalently used in organizational settings and is receiving increased scholarly attention. Various conceptualizations and measurement tools exist, and it is unclear how these theories and measures relate and differ. This paper contributes to practice by providing practical guidelines and limitations for measuring learning agility.
Learning agility was initially conceived as a multidimensional construct comprising people agility, results agility, change agility and mental agility. As the construct has evolved, the dimension structure of the measure has evolved as well. This study addresses a gap in our current understanding of how to conceptualize and measure learning agility.
This case documents an innovative human resource management (HRM) practice adopted by an Australian organization in the energy sector, purposefully introducing lived experience informed “mental health advocate” (MHA) roles into the organization, to address pressing mental health workforce issues. MHA roles provide experiential, first-hand knowledge of experiencing mental health issues, offering a novel, common-sense and impactful perspective on supporting employees with mental health challenges.
Data that informed this case came from desktop research using publicly available resources, as well as a series of conversations with four key stakeholders in the organization. This approach allowed insights into Energy Queensland’s journey towards establishing novel MHA roles to delineate the day-to-day work practice of these roles.
This is a novel HRM practice that has only recently emerged outside of the mental health sector. We discuss key considerations that enabled the success of the roles, including taking an evolutionary perspective, obtaining support from senior executives and relevant stakeholders, making a long-term financial commitment, and providing autonomy and flexibility in role design. This is the first article that documents this innovative practice to offer new insights to HRM scholars, as well as practical guidelines to other organizations in addressing workforce mental health issues.
Drawing on social exchange literature, this study explores the mediating role of affective commitment between employees' assessments of contract breaches and opportunistic silence, along with the invigorating effect of hostile attribution bias.
We tested the hypotheses using multi-wave data collected from employees working in higher education institutions in Pakistan.
Perceived contract breaches elicit intentional, selfish and retaliatory motives of silence, largely because employees lack emotional attachments to their organization. This mechanism is more prominent among employees who tend to blame others and perceive them as antagonistic even when they are not.
For human resource managers, this investigation highlights a crucial feature – affective commitment – by which employees' perceptions of psychological contract breaches facilitate opportunistic silence. Our results suggest that this process is more likely to intensify when employees have distorted thinking, motivating them to attribute the worst motives to their employer's actions.
Perceived contract breaches within universities can have far-reaching societal consequences, affecting trust, reputation, economic stability, and the overall quality and accessibility of education and research. Addressing and preventing such breaches is essential to maintaining the positive societal role of universities.
This study provides novel insights into the process that underlies the connection between perceived contract breach and opportunistic silence by revealing the hitherto overlooked role of employees' hostile attribution bias, which renders them more susceptible to experiencing unfavorable forms of social exchange.
Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.
The paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.
The paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.
The paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.
The metaverse, through artificial intelligence (AI) systems and capabilities, allows considerable data analysis in the workplace, largely exceeding traditional people analytics data collection. While concerns over surveillance and issues associated with privacy and discrimination have been raised, the metaverse has the potential to offer opportunities associated with fairer assessment of employee performance and enhancement of the employee experience, especially with respect to gender and race, inclusiveness and workplace equity. This paper aims at shedding light on the diversity, equity and inclusion (DEI) opportunities and challenges of implementing the metaverse in the workplace, and the role played by AI.
This paper draws on our past research on AI and the metaverse and provides insights addressed to human resources (HR) scholars and practitioners.
Our analysis of AI applications to the metaverse in the workplace sheds light on the ambivalent role of and potential trade-offs that may arise with this emerging technology. If used responsibly, the metaverse can enable positive changes concerning the future of work, which can promote DEI. Yet, the same technology can lead to negative DEI outcomes if implementations occur quickly, unsupervised and with a sole focus on efficiencies and productivity (i.e. collecting metrics, models etc.).
Managers and HR leaders should try to be first movers rather than followers when deciding if (or, better, when) to implement metaverse capabilities in their organizations. But how the metaverse is implemented will be strategic. This involves choices concerning the degree of invasive/pervasive monitoring (internal) as well as make or buy decisions concerning outsourcing AI capabilities.
Our paper is one among few (to date) that discusses AI capabilities in the metaverse at the intersection of the HR and information systems(IS) literature and that specifically tackles DEI issues. Also, we take a “balanced” approach when evaluating the metaverse from a DEI perspective. While most studies either demonize or celebrate these technologies from an ethical and DEI standpoint, we aim to highlight challenges and opportunities, with the goal to guide scholars and practitioners towards a responsible use of the metaverse in organizations.
Early research on overqualification suggested that overqualification is primarily associated with negative attitudes and behavior. As a consequence, hiring practitioners were advised against hiring overqualified job applicants. However, recent studies have revealed that there are several potential positive consequences of overqualification. Given this change in perspective on overqualification, we examine how hiring practitioners nowadays look at overqualified job applicants, and what their considerations are for hiring an overqualified job applicant or not.
We have interviewed 33 hiring practitioners to examine their attitudes and considerations toward hiring overqualified job applicants.
Results show that hiring practitioners are aware of potential positive as well as negative consequences of overqualification and consider a variety of factors to assess how beneficial hiring an overqualified candidate will be. These factors fall under three categories: Individual considerations, interpersonal considerations and contextual considerations.
We show that overqualification is not a stigma anymore and that the decision to hire an overqualified job applicant or not depends on a mixture of factors that are carefully considered. Two of these three considerations transcend the individual level (i.e. the overqualified person), whereas most research and theories on the consequences of overqualification do not go beyond the individual level. As such, our findings call for more theory and research on interpersonal and contextual factors shaping the consequences of overqualification.
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